A wide variety of applications may require capturing, processing, and analysis of images. Many applications (e.g., internet of things (TOT) applications, telemetry applications, surveillance applications, etc.), may require the images to be captured at one location and to be processed at another location. Thus, the images may be captured at certain location, uploaded, and sent to a remote server over cloud for further analysis and storage. However, such applications may present challenges due to limitations in processing power, and network bandwidth between server and edge nodes. For example, many devices may have an always-on connection but the connection speed may be limited. Further, many a times the devices may have good connection speed but the server may have limited processing power. Hence, it may be challenging to store and analyze all the images that are captured by the application.
Existing techniques tries to overcome above challenges by employing traffic prediction model, least load scheduling in real-time, relative load balancing scheduling, and so forth. However, existing techniques fail to balance between connection speed and bandwidth, processing power, load on available resources, and so forth. In other words, existing techniques fail to optimize captured image data for transmission and remote processing.